Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Paul Chukiu

dblp:38/5058 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 87% Energy-efficient computing · 13%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Internet of things and sensor networks
camera sensor networks
0.112006
A storage-centric camera sensor network · SenSys 2006
Internet of things and sensor networks › wireless sensor network › sensor network architecture
storage-centric sensor network
0.112006
A storage-centric camera sensor network · SenSys 2006
Storage systems › flash and SSD › flash memory
flash storage
0.112006
A storage-centric camera sensor network · SenSys 2006
Storage systems › networked storage › storage networking
in-network storage
0.112006
A storage-centric camera sensor network · SenSys 2006

Methods — techniques the papers use, named apart from their topics

motion-triggered image capturing · 0.1
YearPublicationVenuePosition
2009 Ultra-low power data storage for sensor networks
abstract
Local storage is required in many sensor network applications, both for archival of detailed event information, as well as to overcome sensor platform memory constraints. Recent gains in energy efficiency of new-generation NAND flash storage have strengthened the case for in-network storage by data-centric sensor network applications. We argue that current storage solutions offering a simple file system abstraction are inadequate for sensor applications to exploit storage. Instead, we propose Capsule—a rich, flexible and portable object storage abstraction that offers stream, file, array, queue and index storage objects for data storage and retrieval. Further, Capsule supports checkpointing and rollback of object state for fault tolerance. Our experiments demonstrate that Capsule provides platform independence, greater functionality and greater energy efficiency than existing storage solutions.
Gaurav Mathur, Peter Desnoyers, Paul Chukiu, Deepak Ganesan, Prashant J. Shenoy
ACM Trans. Sens. Networks3
2006 A storage-centric camera sensor network
abstract
Improved energy-efficiency and storage capacity of new-generation NAND flash memory makes a compelling case for storage-centric sensor networks. Such a storage-centric sensor network emphasizes the use of platforms with larger storage and more extensive use of the storage capacities on sensors. We demonstrate the feasibility of storage-centric sensor networks using an instance of a storage-centric camera sensor network that is more energy-efficient in comparison to a traditional camera sensor network. We demonstrate multiple camera sensors, each consisting of a Cyclops camera attached to a MicaZ mote, using motion-triggered image capturing. The captured images are archived locally on flash storage and summaries of detected events are transmitted to the base-station. The base-station picks the events of interest from the summaries and requests the original captured image from the sensor as required. The use of high-capacity energy-efficient flash storage at the sensor allows us to trade-off expensive radio communication for cheaper local storage, improving the life-time of the battery and consequently, the life of the storage-centric camera sensor network.
Gaurav Mathur, Paul Chukiu, Peter Desnoyers, Deepak Ganesan, Prashant J. Shenoy
SenSys2